1. Technical Field
The present disclosure relates to three-dimensional (3D) visualization of imaging data, and more particularly to methods and systems for visualizing imaging data using importance values that do not require segmentation.
2. Discussion of Related Art
Users have turned to computers to assist them in the examination and analysis of images of real-world data because of the increasingly fast processing power of modern day computers. For example, within the medical community, radiologists and other professionals who once examined x-rays hung on a light screen now use computers to examine images obtained via computed tomography (CT), computed tomography angiography (CTA), magnetic resonance (MR), ultrasonography, positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic source imaging, and other imaging techniques. Countless other imaging techniques will no doubt arise as medical imaging technology evolves.
Each of the above identified imaging procedures generates volume images, although each relies on a different technology to do so. Thus, CT requires an x-ray source to rapidly rotate around a patient to obtain up to hundreds of electronically stored pictures of the patient. Conversely, for example, MR requires that radio-frequency waves be emitted to cause hydrogen atoms in the body's water to move and release energy, which is then detected and translated into an image. Because each of these techniques penetrates the body of a patient to obtain data, and because the body is three-dimensional, this data represents a three-dimensional image, or volume. In particular, CT and MR both provide three-dimensional “slices” of the body, which can later be electronically reassembled.
Computer graphics images, such as medical images, have typically been modeled through the use of techniques such as surface rendering and other geometric-based techniques. Because of known deficiencies of such techniques, volume-rendering techniques have been developed as a more accurate way to render images based on real-world data. Volume-rendering takes a conceptually intuitive approach to rendering, by assuming that three-dimensional objects are composed of basic volumetric building blocks.
While many physicians still prefer cross-sectional slice images for diagnosis and interpretation of the data, volume rendering can provide a better global spatial impression of the anatomy. This becomes especially important for fusion of CT/CTA with interventional imaging modalities.
In medical procedures such as biopsy and radio frequency ablation, the insertion path of a needle must be carefully planned on the CT scan to avoid critical structures in the patient. During a procedure, ultrasound may be used as an interventional imaging modality to assist in navigating the needle(s) to the correct location. Medical ultrasound mainly depicts borders between various tissue types in a 2D plane oriented from the transducer into the patient's body. If a tracking system is used to locate the ultrasound probe in 3D, and the pre-operative CT scan is aligned (i.e. registered) correctly with the patient coordinate system, volume rendering of the CT data can be merged with a real-time view of the ultrasound plane. The dense 3D information from the CT data helps the physician to relate both the needle and ultrasound plane to the critical anatomical structures, right in the operating theater. Furthermore, planning information like ablation target volumes and margins, optimal needle path, etc., can be visualized.
However, it can be difficult to show enough data from the dense pre-operative scan without occluding the ultrasound image plane. Further, due to the mass of information in a single volume data set, it is preferred that an observer be directed to the most significant parts of the whole data set. Importance-driven rendering can be used to quickly enable cognition of important parts while retaining an impression of the position and the relation of these parts with respect to the rest of the data set. Currently, a data set including a segmentation of the different parts according to their spatial locations is required. However, segmenting a volume data set into different spatial areas is a time consuming and computationally intensive task, which may also require user intervention.
Thus, there exists a need for methods and systems for importance-driven rendering of an object of interest within a volume with minimal occlusion that does not require segmentation of a volume according to spatial locations of the segments.